Hydrological cycle research requires a detailed analysis of the involved parameters to understand watershed behavior comprehensively. In recent decades, both Geographic Information Systems (GIS) and Digital Elevation Models (DEMs) were implemented and took a substantial role in watershed geomorphological parameterization; however, the variability of these instruments remains a challenge, together with high-resolution DEMs being unavailable, requiring digital processing to improve resolution. This research aims to merge DEMs and evaluate GIS geoprocessing algorithms to determine drainage networks and the geomorphological parametrization of a semiarid watershed. DEMs with resolutions of 1.5, 5, 12.5, and 30 m, the Jenson/Domingue (J/D) and Wang/Liu (W/L) fill algorithms; and D8, D, KRA, and MFD flow routing algorithms were used. One of the research findings proved that the divergences of the drainage networks are mainly attributed to filling algorithms and not flow routing algorithms; the shifts between the networks obtained in the processes reach horizontal distances up to 300 m. Since the water movement within the watershed depends on geomorphological characteristics, it is suggested that DEM-based hydrological studies specify both the resolution and the algorithms used in the parametrization to validate the rigidity of the research, improving estimate areas of high hydrological risk.

The supply of drinking water to the population is an important challenge facing humanity, since both surface and underground sources present a great variability of water storage with respect to space and time. This problem is further aggravated in arid and semi-arid areas where rainfall is low and torrential, which makes groundwater the main source of supply; therefore, it is necessary to carry out studies that allow evaluating the evolution of the quantity and quality of water. This study addresses the behavior of groundwater in a semi-arid region, considering the theory of flow systems to identify movement as well as water quality, es determined by a water quality index (WQI), calculated considering arsenic and fluorine. In addition, a quality irrigation classification is used, employing the norms in accordance with international standards and the Mexican Norm, which allows for a comparison. Local, regional, intermediate and mixed flow systems are identified, and the evolution of cations and anions in addition to temperature is examined. It is observed that the drinking water quality index classifies them as excellent in most of the monitored wells (<50), but with a negative evolution. Regarding irrigation, most of the water samples are classified without restriction for the establishment of any type II crop (C₂S₁) and with restrictions for horticultural crops. It is observed that arsenic had values between 0.49 and 61.40 (µg/L) in 2005, while in 2015 they were between 0.10 and 241.30 (µg/L). In addition, fluoride presented values between 0.00 and 2.6 (mg/L) in 2005, while in 2015 they were between 0.28 and 5.40 (mg/L). The correlations between arsenic and fluorine are noted as well as WQI and SAR. A finding in this research was to include arsenic and fluorine in the calculation of the WQI allowing a better interpretation of the quality of water for both human consumption and for agricultural use to based on this make the best decision to control any harmful effects for the population, in addition to identifying the appropriate purification treatment required to control pollutants. It is concluded that arsenic is an element of utmost importance when considering water quality, so it is necessary to examine its evolution and continue to monitor its levels constantly.

Groundwater is the main source of freshwater available for human beings and is generally extracted through wells. The objective of this work was to schematize the groundwater flow systems within the Calera Aquifer through 3D geostatistical estimations of hydraulic head and physico-chemical parameters and the integration of hydrogeological features. The evolution of groundwater during its circulation in the subsoil can be done by identifying different types of flow (local, intermediate, regional, or mixed). Two main approaches have been proposed for the identification of flow systems: explaining the evolution of physico-chemical parameters of water through its interaction with the geologic medium, and using cluster analysis; however, these approaches usually do not consider simultaneously the 3D distribution of hydraulic head, water quality parameters, and the geological media that can be useful to delineate converging flow systems with a differentiated origin. In this paper, the determination of groundwater flow systems within the Calera aquifer in Mexico is supported with 3D representations of these hydrogeological variables besides constructive data of the sampled well. For the case study, the convergence of different flow systems that are not identified through a single cluster analysis was actually noticed by the proposal done in this paper.

This study evaluates the human health risks associated with exposure to arsenic in groundwater from a semi-arid region of Mexico, focusing on concentration levels and their temporal variation. Arsenic concentrations were analyzed using ordinary kriging for spatial interpolation, along with descriptive and inferential statistical methods. Human health risk was assessed through the following two key indicators: the Hazard Quotient (HQ), which estimates non-carcinogenic risk by comparing exposure levels to reference doses and carcinogenic risk (CR), which represents the estimated lifetime probability of developing cancer due to arsenic exposure. The mean arsenic concentration across both study years was 0.0200 mg/L, with median values of 0.0151 mg/L in 2015 and 0.0200 mg/L in 2020. The average HQ was 2.13 in 2015 and 2.17 in 2020, both exceeding the safety threshold of one. Mean CR values were 0.00096 and 0.00097 for 2015 and 2020, respectively, with a consistent median of 0.00072 across both years. A t-test was applied to compare the distributions between years. Both HQ and CR values significantly exceeded the recommended safety limits (p < 0.05), indicating that groundwater in the study area poses a potential carcinogenic and non-carcinogenic health risk. These findings underscore the urgent need for water quality monitoring and the implementation of mitigation measures to safeguard public health in the region.

Water planning in semi-arid regions depends on climate information that resolves both seasonal timing and topographic gradients. This study evaluated 15 CMIP6 models over Zacatecas, Mexico, and produced a 1 km historical dataset for 1985–2014 by statistically refining bias-corrected daily fields from NEX-GDDP-CMIP6. Downscaling was referenced to the CHELSA climatology: temperature was refined using an elevation-informed hybrid spline approach, whereas rainfall was downscaled with geographically weighted regression (GWR) to represent orographic gradients. The resulting fields were assessed against two independent observational baselines: an automated INIFAP network (2004–2014) and a conventional CONAGUA network (1985–2014). For temperature, BCC-CSM2-MR showed the highest performance, with a Pearson correlation of R = 0.94 for both T_max and T_min. A consistent network-dependent bias pattern was identified: the downscaled models overestimated the diurnal temperature range relative to INIFAP but underestimated it relative to CONAGUA, highlighting the influence of instrumentation and observational protocols on model evaluation. For rainfall, ACCESS-ESM1-5 reproduced the seasonal cycle and dominant orographic patterns, with a correlation of R = 0.611 despite the intrinsic stochasticity of semi-arid rainfall. The resulting 1 km fields provide a spatially consistent baseline for regional applications, including stochastic weather generation and impact models in complex semi-arid regions.

Pronounced spatio-temporal variability of rainfall in semi-arid basins remains a central challenge for rainfall–infiltration–runoff modeling, particularly in ungauged or newly instrumented catchments where continuous soil-moisture data are unavailable. This study evaluates the comparative predictive performance of the SCS-CN and Green-Ampt (GA) infiltration methods, implemented within HEC-HMS at the sub-basin scale, using eight rainfall–runoff analysis time windows recorded during the 2020–2025 rainy seasons within a monitoring network operational since October 2019 in an instrumented semi-arid basin in Mexico. A blind-validation framework was adopted: parameters for both methods were derived a priori from tabulated sources indexed by land use, hydrologic soil group, soil textural class, and locally supported by textural analysis at three depths per sub-basin, in situ testing of saturated hydraulic conductivity, and gravimetric determination of field capacity; the initial moisture content required by GA was set equal to the measured field capacity (θ_i = θ_fc) to equate initial conditions between the two methods. Spatially distributed rainfall was captured by four monitoring stations under a one-to-one gauge–sub-basin assignment scheme, with monthly rainfall depth varying from 22.8 to 204.9 mm across the four sub-basins. Both methods reproduced observed discharge with varying levels of agreement: SCS-CN yielded very good performance (Pearson R = 0.95; Nash–Sutcliffe efficiency NSE = 0.76), whereas Green-Ampt yielded moderate correlation but unsatisfactory NSE (R = 0.70; NSE = 0.45) against the Levelogger records. Contrary to the initial expectation that the physically based GA would outperform SCS-CN, SCS-CN yielded substantially higher performance across windows, with the two simulated discharge series differing by a mean absolute deviation of 36.9 m³/s. A systematic sensitivity analysis (±6%, ±10%, ±20% perturbations) revealed an asymmetric response: SCS-CN was highly sensitive to Curve Number perturbations (mean-deviation amplitude 65.9 m³/s), whereas Green-Ampt was nearly insensitive to its compound soil-hydraulic parameterization (amplitude 3.2 m³/s), indicating a structural limitation of the physically based method under blind validation.